R Notebook

library(elsasserlib)
library(ggpubr)
library(reshape)
library(dplyr)
library(ggrepel)

tbl <- read.table('Kumar_2020_master_gene_table_rnaseq_shrunk_plus_annotations.tsv',sep = "\t",header = T)
tbl <- tbl[!duplicated(tbl$name),]


tbl$sig_Ni_EZH2i_up <- (tbl$RNASeq_DS_EZH2i_vs_Ni_log2FoldChange > 0) & (tbl$RNASeq_DS_EZH2i_vs_Ni_padj < 0.05)
tbl$sig_Ni_EZH2i_down <- (tbl$RNASeq_DS_EZH2i_vs_Ni_log2FoldChange < -0) & (tbl$RNASeq_DS_EZH2i_vs_Ni_padj < 0.05)

tbl$Lanner_germlayer[tbl$name %in% c("MSX2")] <- "EarlyTE"
tbl$Lanner_germlayer[tbl$name %in% c("GATA2","GATA3","KRT7","XAGE2","NUAK2","EPAS1")] <- "TE"
tbl$Lanner_germlayer[tbl$name %in% c("VGLL1","CGB5")] <- "CTB"
tbl$Lanner_germlayer[tbl$name %in% c("SLC40A1","SDC1","CLDN4")] <- "STB"
tbl$sig_summary <- "ns"
tbl$sig_summary[tbl$sig_Ni_EZH2i_up] <- "Ni_EZH2i_up"
tbl$sig_summary[tbl$sig_Ni_EZH2i_down] <- "Ni_EZH2i_down"

lanner.set <- select(tbl[tbl$Lanner_germlayer != "None",], "name","Lanner_germlayer","sig_summary", contains("Log2FoldChange") & contains("RNASeq") )

lanner.set$Lanner_germlayer <- factor(lanner.set$Lanner_germlayer, levels= c("EightCells","Morula","ICM","EarlyBlastocyst","Early","EarlyEpi","Early/Mid Epi","MidEpi","LateEpi","PE","EarlyTE","TE","LateTE","CTB","STB","EVT","Amnion","YsMes","AxMes","AdvMes","NasMes","EmMes","Endoderm","PriS")
)

goi <- c("GATA3","GATA2","CDX2","TP63","CGA","CGB","CGB5","POU5F1","DPPA3","VGLL1","BMP4","VIM","DPPA2","NANOG","SOX2","KRT19","FGF4","TFAP2A","KRT7","ENPEP","IGF2","FRZB","ERP27","KRT23","DNMT3A","DNMT3L","XAGE2","HAND1","KRT18","KLF6","NUAK2","EPAS1","MSX2", "SDC1", "SLC40A1","CLDN4")

ggstripchart(lanner.set,x="Lanner_germlayer",y="RNASeq_DS_EZH2i_vs_Ni_log2FoldChange", 
             orientation="horizontal", 
             color = "sig_summary", 
             palette=c("#88AAFF","#EE8844","#DDDDDD"), 
             size = 1, jitter=0.2,
             ggtheme=theme_bw(),
             label="name", font.label = list(size = 7), repel=T, label.select = goi , label.rectangle = F) + scale_x_discrete(limits=rev)  

ggsave("lanner_germlayer_naive_strip.pdf")

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ggstripchart(lanner.set,x="Lanner_germlayer",y="RNASeq_DS_EZH2i_vs_Pr_log2FoldChange", 
             orientation="horizontal", 
             color = "sig_summary", 
             palette=c("#88AAFF","#EE8844","#DDDDDD"), 
             size = 1, jitter=0.2,
             ggtheme=theme_bw(),
             label="name", font.label = list(size = 7), repel=T, label.select = goi , label.rectangle = F) + scale_x_discrete(limits=rev)  

ggsave("lanner_germlayer_primed_strip.pdf")

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ggstripchart(lanner.set,x="Lanner_germlayer",y="RNASeq_DS_Pr_vs_Ni_log2FoldChange", 
             orientation="horizontal", 
             color = "sig_summary", 
             palette=c("#88AAFF","#EE8844","#DDDDDD"), 
             size = 1, jitter=0.2,
             ggtheme=theme_bw(),
             label="name", font.label = list(size = 7), repel=T, label.select = goi , label.rectangle = F) + scale_x_discrete(limits=rev)  

ggsave("lanner_germlayer_ni_vs_pr_strip.pdf")

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tbl <- read.table('Kumar_2020_master_gene_table_rnaseq_shrunk_plus_annotations.tsv',sep = "\t",header = T)
tbl <- tbl[!duplicated(tbl$name),]


tbl$sig_Ni_EZH2i_up <- (tbl$RNASeq_DS_EZH2i_vs_Ni_log2FoldChange > 1) & (tbl$RNASeq_DS_EZH2i_vs_Ni_padj < 0.05)
tbl$sig_Ni_EZH2i_down <- (tbl$RNASeq_DS_EZH2i_vs_Ni_log2FoldChange < -1) & (tbl$RNASeq_DS_EZH2i_vs_Ni_padj < 0.05)
tbl$sig_Pr_EZH2i_up <- (tbl$RNASeq_DS_EZH2i_vs_Pr_log2FoldChange > 1) & (tbl$RNASeq_DS_EZH2i_vs_Pr_padj < 0.05)
tbl$sig_Pr_EZH2i_down <- (tbl$RNASeq_DS_EZH2i_vs_Pr_log2FoldChange < -1) & (tbl$RNASeq_DS_EZH2i_vs_Pr_padj < 0.05)


tbl$sig_summary <- "ns"
tbl$sig_summary[tbl$sig_Ni_EZH2i_up] <- "Ni_EZH2i_up"
tbl$sig_summary[tbl$sig_Ni_EZH2i_down] <- "Ni_EZH2i_down"
tbl$sig_summary[tbl$sig_Pr_EZH2i_up] <- "Pr_EZH2i_up"
tbl$sig_summary[tbl$sig_Pr_EZH2i_up] <- "Pr_EZH2i_down"
tbl$sig_summary[tbl$sig_Ni_EZH2i_up & tbl$sig_Pr_EZH2i_up] <- "NP_EZH2i_up"
tbl$sig_summary[tbl$sig_Ni_EZH2i_down & tbl$sig_Pr_EZH2i_down] <- "NP_EZH2i_down"

lanner.set <- select(tbl[tbl$Lanner_germlayer != "None",], c("Lanner_germlayer","sig_summary"))
mdf <- melt(table(lanner.set))

mdf$sig_summary <- as.factor(mdf$sig_summary)

ggbarplot(mdf,x="Lanner_germlayer",y="value",fill="sig_summary", palette= "Paired",orientation="horizontal")

lanner.set <- select(tbl[tbl$Lanner_germlayer != "None",], "Lanner_germlayer","name", contains("mean_cov"))

lanner.set$K4K27ratio_Ni_mean_cov <- log2(lanner.set$H3K4m3_Ni_mean_cov / lanner.set$H3K27m3_Ni_mean_cov)
lanner.set$K4K27ratio_Pr_mean_cov <- log2(lanner.set$H3K4m3_Pr_mean_cov / lanner.set$H3K27m3_Pr_mean_cov)

mdf <- melt(lanner.set)

Using Lanner_germlayer, name as id variables

mdf <- cbind(mdf,colsplit(mdf$variable,"_",c("mark","state","treatment")))

number of columns of result is not a multiple of vector length (arg 511)

mdf$treatment <- gsub(mdf$treatment,pattern = "mean",replacement = "NT") 
mdf$sample <- paste0(mdf$state," ",mdf$treatment)

ggstripchart(mdf[mdf$mark=="H3K4m3",],x="sample",y="value",color="sample", facet.by = "Lanner_germlayer")

tbl <- read.table('Kumar_2020_master_gene_table_rnaseq_shrunk_plus_annotations.tsv',sep = "\t",header = T)
tbl <- tbl[!duplicated(tbl$name),]

tbl$K4_EZH2i_up <- (tbl$RNASeq_DS_EZH2i_vs_Ni_log2FoldChange > 0) & (tbl$RNASeq_DS_EZH2i_vs_Ni_padj < 0.05) & (tbl$H3K4m3_Ni_EZH2i_mean_cov/tbl$H3K4m3_Ni_mean_cov > 2)

K4.set <- select(tbl[tbl$K4_EZH2i_up,], "Lanner_germlayer","name", contains("mean_cov"))

mdf <- melt(K4.set)

Using Lanner_germlayer, name as id variables

mdf <- cbind(mdf,colsplit(mdf$variable,"_",c("mark","state","treatment")))

number of columns of result is not a multiple of vector length (arg 344)

mdf$treatment <- gsub(mdf$treatment,pattern = "mean",replacement = "NT") 
mdf$sample <- paste0(mdf$state," ",mdf$treatment)

ggstripchart(mdf[mdf$mark=="H3K4m3",],x="sample",y="value",fill="sample", label="name",repel=T)

lanner.set <- select(tbl[tbl$Lanner_germlayer != "None",], "Lanner_germlayer",contains("TPM") & contains("R1") )

mdf <- melt(lanner.set)

Using Lanner_germlayer as id variables

mdf <- cbind(mdf,colsplit(mdf$variable,"_",c("RNASeq","TPM","state","treatment")))

number of columns of result is not a multiple of vector length (arg 511)

mdf$treatment <- gsub(mdf$treatment,pattern = "mean",replacement = "NT") 
mdf$sample <- paste0(mdf$state," ",mdf$treatment)
mdf$lg2 <- log2(mdf$value)

ggboxplot(mdf,x="sample",y="lg2",fill="sample", facet.by = "Lanner_germlayer")

ggpaired(lanner.set,cond1="RNASeq_TPM_Ni_R1",cond2="RNASeq_TPM_Ni_EZH2i_R1",facet.by = "Lanner_germlayer",fill = "condition") + scale_y_continuous(trans='log2')

ggsave("pairplot_RNASeq.pdf")

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ggpaired(lanner.set,cond1="RNASeq_TPM_Ni_R1",cond2="RNASeq_TPM_Ni_EZH2i_R1",facet.by = "Lanner_germlayer",fill = "condition") + scale_y_continuous(trans='log2')

ggsave("pairplot_RNASeq.pdf")

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Intermediate population

EZH2i.tbl <- data.frame(name=tbl$name,lfc=tbl$RNASeq_DS_EZH2i_vs_Ni_log2FoldChange)

Messmer_tbl <- read.table("Messmer_CellRep2019_TableS2_IntermediatePop.tsv",header=T, sep="\t")
inter.tbl <- select(Messmer_tbl[Messmer_tbl$P.Value<0.0001 ,],"Gene","vsNaive")
colnames(inter.tbl) <- c("name","lfc")

inter.tbl <- inter.tbl[inter.tbl$name %in% EZH2i.tbl$name,]
inter.tbl <- inter.tbl[order(inter.tbl$lfc),]
#inter.tbl <- rbind(head(inter.tbl,50),tail(inter.tbl,50))

inter.down50 <- head(inter.tbl$name,50)
inter.up50 <- tail(inter.tbl$name,50)
inter.down25 <- head(inter.tbl$name,25)
inter.up25 <- tail(inter.tbl$name,25)

write.table(inter.down25,"Messmer_intermediate_down.top25.txt",sep="\t",quote=F,row.names=F,col.names=F)
write.table(inter.up25,"Messmer_intermediate_up.top25.txt",sep="\t",quote=F,row.names=F,col.names=F)
 
EZH2i.tbl <- EZH2i.tbl[EZH2i.tbl$name %in% inter.tbl$name,]

EZH2i.tbl$set <- "Ni_EZH2i" 
inter.tbl$set <- "Messmer_intermediate" 

mdf <- rbind(EZH2i.tbl,inter.tbl)
mdf$lfc <- mdf$lfc

cor.tbl <- cast(mdf,formula = name ~ set, value = "lfc")

ggscatter(cor.tbl <- cast(mdf,formula = name ~ set, value = "lfc")
,x="Ni_EZH2i",y="Messmer_intermediate", size=1, add = "reg.line", conf.int = TRUE) +  stat_cor(method = "pearson", label.x = 3) + scale_x_continuous(limits=c(-7,7)) + scale_y_continuous(limits=c(-7,7))

cor.tbl

cor.tbl$quantile <- cut(cor.tbl$Messmer_intermediate, 10, include.lowest=TRUE, labels = seq(1,10))


ggboxplot(cor.tbl,x = "quantile",y="Ni_EZH2i",fill = "quantile")

EZH2i.tbl <- data.frame(name=tbl$name,lfc=tbl$RNASeq_DS_EZH2i_vs_Ni_log2FoldChange)
inter_up.tbl <- EZH2i.tbl[EZH2i.tbl$name %in% inter.up50,]
inter_down.tbl <- EZH2i.tbl[EZH2i.tbl$name %in% inter.down50,]

inter_up.tbl$set <- "Intermediate"

ggscatter(test,x="Ni_EZH2i",y="Messmer_intermediate", size=1, add = "reg.line", conf.int = TRUE) +  stat_cor(method = "pearson", label.x = 3)

Error in ggscatter(test, x = "Ni_EZH2i", y = "Messmer_intermediate", size = 1,  : 
  object 'test' not found

inter.set <- select(tbl[tbl$name %in% inter.down50,], "Lanner_germlayer",contains("TPM") & contains("R1"))

mdf <- melt(inter.set)

Using Lanner_germlayer as id variables

mdf <- cbind(mdf,colsplit(mdf$variable,"_",c("RNASeq","TPM","state","treatment")))

number of columns of result is not a multiple of vector length (arg 51)

mdf$treatment <- gsub(mdf$treatment,pattern = "mean",replacement = "NT") 
mdf$sample <- paste0(mdf$state," ",mdf$treatment)
mdf$lg2 <- log2(mdf$value)

ggpaired(inter.set,cond1="RNASeq_TPM_Ni_R1",cond2="RNASeq_TPM_Ni_EZH2i_R1",fill = "condition") + scale_y_continuous(trans='log2')

inter.set <- select(tbl[tbl$name %in% inter.up50,], "Lanner_germlayer",contains("TPM") & contains("R1"))

mdf <- melt(inter.set)

Using Lanner_germlayer as id variables

mdf$value <- log2(mdf$value)

mdf <- cbind(mdf,colsplit(mdf$variable,"_",c("RNASeq","TPM","state","treatment")))

number of columns of result is not a multiple of vector length (arg 51)

mdf$treatment <- gsub(mdf$treatment,pattern = "mean",replacement = "NT") 
mdf$sample <- paste0(mdf$state," ",mdf$treatment)
mdf$lg2 <- log2(mdf$value)

NaNs produced

ggpaired(inter.set,cond1="RNASeq_TPM_Ni_R1",cond2="RNASeq_TPM_Ni_EZH2i_R1",fill = "condition") + scale_y_continuous(trans='log2')

Messmer_pop <- unlist(read.table(file = "Messmer_intermediate_top500.txt"))
KRT7_genes <- unlist(read.table('Zuchida2020_KRT7pop_genes.txt',sep = "\t",header = F))

inter.set <- select(tbl[tbl$name %in% inter.up25,], "Lanner_germlayer","name",contains("mean_cov"))

inter.set$K4K27ratio_Ni_mean_cov <- log2(inter.set$H3K4m3_Ni_mean_cov / inter.set$H3K27m3_Ni_mean_cov)
inter.set$K4K27ratio_Pr_mean_cov <- log2(inter.set$H3K4m3_Pr_mean_cov / inter.set$H3K27m3_Pr_mean_cov)

mdf <- melt(inter.set)

Using Lanner_germlayer, name as id variables

mdf <- cbind(mdf,colsplit(mdf$variable,"_",c("mark","state","treatment")))

number of columns of result is not a multiple of vector length (arg 26)

mdf$treatment <- gsub(mdf$treatment,pattern = "mean",replacement = "NT") 
mdf$sample <- paste0(mdf$state," ",mdf$treatment)

ggstripchart(mdf[mdf$mark=="H3K27m3",],x="sample",y="value",color="sample",label="name",repel=T)

ggstripchart(mdf,x="sample",y="value",fill="sample",add="boxplot",size=0.8,alpha=0.4,facet.by="dir")  + scale_y_continuous(trans="log2")

ggsave("Messmer_top50_RNASeq_boxplot.pdf")

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ggstripchart(mdf[mdf$state=="Ni",],x="sample",y="value",fill="sample",add="boxplot",size=0.8,alpha=0.4,facet.by="dir")  + scale_y_continuous(trans="log2")

ggsave("Messmer_top50_RNASeq_boxplot_Ni_only.pdf")

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inter.up50 <- unlist(read.table(file = "Messmer_intermediate_up.top50.txt"))
inter.down0 <- unlist(read.table(file = "Messmer_intermediate_down.top50.txt"))

inter.set.up <- select(tbl[tbl$name %in% inter.up50,], "Lanner_germlayer",contains("mean_cov"))
inter.set.down <- select(tbl[tbl$name %in% inter.down50,], "Lanner_germlayer",contains("mean_cov"))
inter.set.up$dir <- "up"
inter.set.down$dir <- "down"
inter.set <- rbind(inter.set.up,inter.set.down)

mdf <- melt(inter.set)

Using Lanner_germlayer, dir as id variables

mdf <- cbind(mdf,colsplit(mdf$variable,"_",c("mark","state","treatment")))

number of columns of result is not a multiple of vector length (arg 101)

mdf$treatment <- gsub(mdf$treatment,pattern = "mean",replacement = "NT") 
mdf$sample <- paste0(mdf$state," ",mdf$treatment)

mdf_updown <- mdf

ggpaired(inter.set,cond1="H3K27m3_Ni_mean_cov",cond2="H3K27m3_Pr_mean_cov",fill = "condition", facet.by = "dir")

ggstripchart(mdf[mdf$mark=="H3K27m3",],x="sample",y="value",fill="sample",add="boxplot",size=0.8,alpha=0.4,facet.by="dir")

ggsave("Messmer_top50_H3K27me3_boxplot.pdf")

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ggstripchart(mdf[mdf$mark=="H3K4m3",],x="sample",y="value",fill="sample",add="boxplot",size=0.8,alpha=0.4,facet.by="dir")

ggsave("Messmer_top50_H3K4me3_boxplot.pdf")

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ggstripchart(mdf[mdf$mark=="H2Aub",],x="sample",y="value",fill="sample",add="boxplot",size=0.8,alpha=0.4,facet.by="dir")

ggsave("Messmer_top50_H2Aub_boxplot.pdf")

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Messmer_pop <- unlist(read.table(file = "Messmer_intermediate_up.top50.txt"))

inter.set <- select(tbl[tbl$name %in% inter.down50,], "Lanner_germlayer",contains("mean_cov"))

inter.set$K4K27ratio_Ni_mean_cov <- log2(inter.set$H3K4m3_Ni_mean_cov / inter.set$H3K27m3_Ni_mean_cov)
inter.set$K4K27ratio_Pr_mean_cov <- log2(inter.set$H3K4m3_Pr_mean_cov / inter.set$H3K27m3_Pr_mean_cov)

mdf <- melt(inter.set)

Using Lanner_germlayer as id variables

mdf <- cbind(mdf,colsplit(mdf$variable,"_",c("mark","state","treatment")))

number of columns of result is not a multiple of vector length (arg 51)

mdf$treatment <- gsub(mdf$treatment,pattern = "mean",replacement = "NT") 
mdf$sample <- paste0(mdf$state," ",mdf$treatment)

ggpaired(inter.set,cond1="H3K27m3_Ni_mean_cov",cond2="H3K27m3_Pr_mean_cov",fill = "condition")

ggstripchart(mdf[mdf$mark=="H3K27m3",],x="sample",y="value",fill="sample",add="boxplot",size=0.8,alpha=0.4)

ggsave("Messmer_top50down_H3K27me3_boxplot.pdf")

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ggstripchart(mdf[mdf$mark=="H3K4m3",],x="sample",y="value",fill="sample",add="boxplot",size=0.8,alpha=0.4)

ggsave("Messmer_top50down_H3K4me3_boxplot.pdf")

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ggstripchart(mdf[mdf$mark=="H2Aub",],x="sample",y="value",fill="sample",add="boxplot",size=0.8,alpha=0.4)

ggsave("Messmer_top50down_H2Aub_boxplot.pdf")

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